Classifying Advanced Driver Assistance System (ADAS) Activation from Multimodal Driving Data: A Real-World Study

TL;DR

This study explores how to detect when driver assistance systems are active using real-world driving data, offering insights into driver behavior and system performance.

Contribution

The paper introduces a novel multimodal dataset and classification methods for detecting ADAS activation in real-world driving.

Findings

ADAS activation is associated with reduced steering variability and more stable speed control.

A multimodal dataset combining CAN-bus and IMU signals was collected and analyzed for ADAS detection.

Lightweight classification pipelines achieved moderate accuracy in distinguishing ADAS operation.

Abstract

Identifying the activation status of advanced driver assistance systems (ADAS) in real-world driving environments is crucial for safety, responsibility attribution, and accident forensics. Unlike prior studies that primarily rely on simulation-based settings or unsynchronized data, we collected a multimodal dataset comprising synchronized controller area network (CAN)-bus and smartphone-based inertial measurement unit (IMU) signals from drivers on consistent highway sections under both ADAS-enabled and manual modes. Using these data, we developed lightweight classification pipelines based on statistical and deep learning approaches to explore the feasibility of distinguishing ADAS operation. Our analyses revealed systematic behavioral differences between modes, particularly in speed regulation and steering stability, highlighting how ADAS reduces steering variability and stabilizes speed control. Although classification accuracy was moderate, this study provides one of the first data-driven demonstrations of ADAS status detection under naturalistic conditions. Beyond classification, the released dataset enables systematic behavioral analysis and offers a valuable resource for advancing research on driver monitoring, adaptive ADAS algorithms, and accident forensics.

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